class_docstr: A generic Linear Gaussian State Space Model parameterized with arbitrary time invariant
  transition and observation dynamics. The targets are (implicitly) assumed to be evenly
  spaced in time. Training and inference are logarithmic in the length of the time series T.
  
  :param int obs_dim: The dimension of the targets at each time step.
  :param int state_dim: The dimension of latent state at each time step.
  :param bool learnable_observation_loc: whether the mean of the observation model should be learned or not;
      defaults to False.
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code: def _get_trans_dist(self):
      loc = self.obs_matrix.new_zeros(self.state_dim)
      return MultivariateNormal(
          loc, self.trans_noise_scale_sq.diag_embed()
      )
  
file_path: pyro/pyro/contrib/timeseries/lgssm.py
imports: import torch
  import torch.nn
  from torch.distributions import MultivariateNormal, constraints
  import pyro.distributions
  from pyro.contrib.timeseries.base import TimeSeriesModel
  from pyro.nn import PyroParam, pyro_method
  from pyro.ops.tensor_utils import repeated_matmul
label_desc: Use image node_id 4 for calling the GenericLGSSM obj's underlying member method code with example usage: obj._get_trans_dist() and returns: MultivariateNormal